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In order to reduce the risk factors arising due to chronic kidney disease, it is essential to be diagnosed in the earlier stages itself. This work proposes an automated chronic kidney disease detection based on the textural features of the kidney using a hybrid random forest classifier from 2D ultrasound kidney images. The proposed classifier is compared with the other competing machine learning classifiers through experimenting on a dataset of 150 images and gives a better accuracy of [Formula: see text] with [Formula: see text] of recall and precision, thus proving it to be promising in detecting CKD noninvasively in the early stages.<\/jats:p>","DOI":"10.1142\/s0218001422560109","type":"journal-article","created":{"date-parts":[[2022,5,15]],"date-time":"2022-05-15T03:49:20Z","timestamp":1652586560000},"source":"Crossref","is-referenced-by-count":2,"title":["A Hybrid Random Forest Classifier for Chronic Kidney Disease Prediction from 2D Ultrasound Kidney Images"],"prefix":"10.1142","volume":"36","author":[{"given":"Deepthy Mary","family":"Alex","sequence":"first","affiliation":[{"name":"Department of ECE, Karunya Institute of Technology and Sciences, Karunya Nagar, Coimbatore - 641114, Tamil Nadu, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0655-4544","authenticated-orcid":false,"given":"D. 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